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pmid:35211137

Virulence Biomarkers of Bursaphelenchus xylophilus: A Proteomic Approach.

Joana M S Cardoso · Sandra I Anjo · Bruno Manadas · Hugo Silva · Isabel Abrantes · Katsunori Nakamura · Luís Fonseca

Frontiers in plant science · 2021 doi:10.3389/fpls.2021.822289

Paper

complete

Experiment

partial

Data

partial

Analysis

partial

Execution

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Virulence Biomarkers of Bursaphelenchus xylophilus: A Proteomic Approach.
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10.3389/fpls.2021.822289
pmid
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Joana M S Cardoso
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Sandra I Anjo
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Bruno Manadas
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Hugo Silva
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Isabel Abrantes
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Katsunori Nakamura
[6]
Luís Fonseca
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Frontiers in plant science
year
2021
abstract
The pinewood nematode (PWN), Bursaphelenchus xylophilus, one of the most serious forest pests worldwide, is considered the causal agent of the pine wilt disease (PWD). The main host species belong to the genus Pinus, and a variation in the susceptibility of several pine species to PWN infection is well-known. It is also recognized that there is variation in the virulence among B. xylophilus isolates. In the present study, we applied a quantitative mass spectrometry-based proteomics approach to perform a deep characterization of proteomic changes across two B. xylophilus isolates with different virulence from different hosts and geographical origins. A total of 1,456 proteins were quantified and compared in the two isolates secretomes, and a total of 2,741 proteins were quantified and compared in the nematode proteomes in pine tree extract and fungus stimuli conditions. From the proteomic analyses, a group of proteins was selected and identified as potential virulence biomarkers and shed light on putative most pathogenic proteins of this plant-parasitic nematode. Proteomic data are available via ProteomeXchange with identifier PXD029377.
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A Mann-Whitney U test performed in InfernoRDN (version 1.1.5581.33355) (Polpitiya et al., 2008) was used in the case of the secretome samples. For the proteome analysis, a Kruskal–Wallis test was made to select the proteins, which were statistically different between all the comparisons, followed by the Dunn’s test of Multiple Comparisons, with Benjamini–Hochberg p-value adjustment, to determine in which comparisons statistical differences were found.
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